216 citations · 298 across the 6 of their papers we have counts for
7 papers
Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments
Andreas van Cranenburgh, Corina Koolen
It is an open question to what extent perceptions of literary quality are derived from text-intrinsic versus social factors. While supervised models can predict literary quality ra…
A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News
Corbèn Poot, Andreas van Cranenburgh
We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results…
Embarrassingly Simple Unsupervised Aspect Extraction
Stéphan Tulkens, Andreas van Cranenburgh
We present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore s…
What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models
Wietse de Vries, Andreas van Cranenburgh, Malvina Nissim
Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers. To…
BERTje: A Dutch BERT Model
Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza +3
The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks. Using the same architectur…
A Data-Oriented Model of Literary Language
Andreas van Cranenburgh, Rens Bod
We consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, m…